code: def get_gpu_memory():
      bash_command = "nvidia-smi --query-gpu=memory.total --format=csv"
      output = subprocess.check_output(bash_command, shell=True).decode(
          "utf-8"
      )
      lines = output.split("\n")
      lines.pop(0)
      gpus_memory = []
      for line in lines:
          tokens = line.split(" ")
          if len(tokens) > 1:
              gpus_memory.append(int(tokens[0]))
      gpus_memory.sort()
      max_gpu_memory = ceil(gpus_memory[-1] / 1024)
  
      return max_gpu_memory
  
imports: from cuml.testing.utils import create_synthetic_dataset
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn import datasets
  from sklearn.datasets import make_regression
  from sklearn.datasets import make_classification
  from sklearn.datasets import fetch_california_housing
  from sklearn.datasets import fetch_20newsgroups
  from sklearn.utils import Bunch
  from datetime import timedelta
  from math import ceil
  import hypothesis
  from cuml.internals.safe_imports import gpu_only_import
  import pytest
  import os
  import subprocess
  import pandas
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 13 for calling a global function with example usage: get_gpu_memory() and returns: max_gpu_memory